



'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCAmelCase_  :							Union[str, Any]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							Dict             =    {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : int						  =					'marian'
						_lowerCAmelCase  : Optional[int]						  =					['past_key_values']
						_lowerCAmelCase  : Optional[int]						  =					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







						def __init__(	self :   Union[str, Any]					,     lowerCAmelCase__ :   Dict=5_81_01					,     lowerCAmelCase__ :   int=None					,     lowerCAmelCase__ :   List[str]=10_24					,     lowerCAmelCase__ :   Union[str, Any]=12					,     lowerCAmelCase__ :   Dict=40_96					,     lowerCAmelCase__ :   List[Any]=16					,     lowerCAmelCase__ :   Union[str, Any]=12					,     lowerCAmelCase__ :   Union[str, Any]=40_96					,     lowerCAmelCase__ :   Optional[Any]=16					,     lowerCAmelCase__ :   Tuple=0.0					,     lowerCAmelCase__ :   Tuple=0.0					,     lowerCAmelCase__ :   List[Any]=True					,     lowerCAmelCase__ :   List[str]=True					,     lowerCAmelCase__ :   Union[str, Any]="gelu"					,     lowerCAmelCase__ :   Any=10_24					,     lowerCAmelCase__ :   Dict=0.1					,     lowerCAmelCase__ :   Optional[Any]=0.0					,     lowerCAmelCase__ :   str=0.0					,     lowerCAmelCase__ :   int=0.02					,     lowerCAmelCase__ :   Union[str, Any]=5_81_00					,     lowerCAmelCase__ :   str=False					,     lowerCAmelCase__ :   int=5_81_00					,     lowerCAmelCase__ :   Union[str, Any]=0					,     lowerCAmelCase__ :   Dict=0					,     lowerCAmelCase__ :   str=True					,     **lowerCAmelCase__ :   List[Any]					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      vocab_size
													SCREAMING_SNAKE_CASE       :   List[str]         =      decoder_vocab_size or vocab_size
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      max_position_embeddings
													SCREAMING_SNAKE_CASE       :   List[str]         =      d_model
													SCREAMING_SNAKE_CASE       :   List[str]         =      encoder_ffn_dim
													SCREAMING_SNAKE_CASE       :   List[str]         =      encoder_layers
													SCREAMING_SNAKE_CASE       :   Any         =      encoder_attention_heads
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      decoder_ffn_dim
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      decoder_layers
													SCREAMING_SNAKE_CASE       :   Tuple         =      decoder_attention_heads
													SCREAMING_SNAKE_CASE       :   Any         =      dropout
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      attention_dropout
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      activation_dropout
													SCREAMING_SNAKE_CASE       :   int         =      activation_function
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      init_std
													SCREAMING_SNAKE_CASE       :   Dict         =      encoder_layerdrop
													SCREAMING_SNAKE_CASE       :   Tuple         =      decoder_layerdrop
													SCREAMING_SNAKE_CASE       :   Dict         =      use_cache
													SCREAMING_SNAKE_CASE       :   int         =      encoder_layers
													SCREAMING_SNAKE_CASE       :   str         =      scale_embedding  # scale factor will be sqrt(d_model) if True
													SCREAMING_SNAKE_CASE       :   List[str]         =      share_encoder_decoder_embeddings
													super().__init__(
													    pad_token_id=lowerCAmelCase__					,     eos_token_id=lowerCAmelCase__					,     is_encoder_decoder=lowerCAmelCase__					,     decoder_start_token_id=lowerCAmelCase__					,     forced_eos_token_id=lowerCAmelCase__					,     **lowerCAmelCase__					,     )


class 						lowerCamelCase_				(       snake_case_      ):







						@property
						# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													if self.task in ["default", "seq2seq-lm"]:
																				SCREAMING_SNAKE_CASE       :   Dict         =      OrderedDict(
																				    [
																				        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																				        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																				    ]					)

																				if self.use_past:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      {0: '''batch'''}
																											SCREAMING_SNAKE_CASE       :   List[str]         =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																				else:
																											SCREAMING_SNAKE_CASE       :   Any         =      {0: '''batch''', 1: '''decoder_sequence'''}
																											SCREAMING_SNAKE_CASE       :   int         =      {0: '''batch''', 1: '''decoder_sequence'''}

																				if self.use_past:
																											self.fill_with_past_key_values_(lowerCAmelCase__					,     direction='''inputs'''					)
													elif self.task == "causal-lm":
																				# TODO: figure this case out.
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      OrderedDict(
																				    [
																				        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																				        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																				    ]					)
																				if self.use_past:
																											SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Tuple         =      self.num_layers
																											for i in range(lowerCAmelCase__					):
																																		SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {0: '''batch''', 2: '''past_sequence + sequence'''}
																																		SCREAMING_SNAKE_CASE       :   Optional[int]         =      {0: '''batch''', 2: '''past_sequence + sequence'''}
													else:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      OrderedDict(
																				    [
																				        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																				        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																				        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
																				        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
																				    ]					)

													return common_inputs







						@property
						# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													if self.task in ["default", "seq2seq-lm"]:
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      super().outputs
													else:
																				SCREAMING_SNAKE_CASE       :   Any         =      super(lowerCAmelCase__					,     self					).outputs
																				if self.use_past:
																											SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.num_layers
																											for i in range(lowerCAmelCase__					):
																																		SCREAMING_SNAKE_CASE       :   List[str]         =      {0: '''batch''', 2: '''past_sequence + sequence'''}
																																		SCREAMING_SNAKE_CASE       :   Tuple         =      {0: '''batch''', 2: '''past_sequence + sequence'''}
													return common_outputs







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   PreTrainedTokenizer					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   bool = False					,     lowerCAmelCase__ :   Optional[TensorType] = None					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self._generate_dummy_inputs_for_encoder_and_decoder(
													    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)

													# Generate decoder inputs
													SCREAMING_SNAKE_CASE       :   int         =      seq_length if not self.use_past else 1
													SCREAMING_SNAKE_CASE       :   str         =      self._generate_dummy_inputs_for_encoder_and_decoder(
													    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
													SCREAMING_SNAKE_CASE       :   List[str]         =      dict(**lowerCAmelCase__					,     **lowerCAmelCase__					)

													if self.use_past:
																				if not is_torch_available():
																											raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
																				else:
																											import torch
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      common_inputs['''input_ids'''].shape
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      common_inputs['''decoder_input_ids'''].shape[1]
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      self.num_attention_heads
																				SCREAMING_SNAKE_CASE       :   int         =      (
																				    batch,
																				    num_encoder_attention_heads,
																				    encoder_seq_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      decoder_seq_length + 3
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      (
																				    batch,
																				    num_decoder_attention_heads,
																				    decoder_past_length,
																				    self._config.hidden_size // num_decoder_attention_heads,
																				)

																				SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.cat(
																				    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase__					,     lowerCAmelCase__					)]					,     dim=1					)

																				SCREAMING_SNAKE_CASE       :   List[str]         =      []
																				# If the number of encoder and decoder layers are present in the model configuration, both are considered
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Tuple         =      self.num_layers
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      min(lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   List[str]         =      max(lowerCAmelCase__					,     lowerCAmelCase__					) - min_num_layers
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																				for _ in range(lowerCAmelCase__					):
																											common_inputs["past_key_values"].append(
																											    (
																											        torch.zeros(lowerCAmelCase__					),
																											        torch.zeros(lowerCAmelCase__					),
																											        torch.zeros(lowerCAmelCase__					),
																											        torch.zeros(lowerCAmelCase__					),
																											    )					)
																				# TODO: test this.
																				SCREAMING_SNAKE_CASE       :   List[str]         =      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																				for _ in range(lowerCAmelCase__					,     lowerCAmelCase__					):
																											common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase__					), torch.zeros(lowerCAmelCase__					))					)
													return common_inputs







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   PreTrainedTokenizer					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   bool = False					,     lowerCAmelCase__ :   Optional[TensorType] = None					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      self._generate_dummy_inputs_for_encoder_and_decoder(
													    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)

													if self.use_past:
																				if not is_torch_available():
																											raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
																				else:
																											import torch
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      common_inputs['''input_ids'''].shape
																				# Not using the same length for past_key_values
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      seqlen + 2
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.num_layers
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      self.num_attention_heads
																				SCREAMING_SNAKE_CASE       :   int         =      (
																				    batch,
																				    num_encoder_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)

																				SCREAMING_SNAKE_CASE       :   Dict         =      common_inputs['''attention_mask'''].dtype
																				SCREAMING_SNAKE_CASE       :   List[str]         =      torch.cat(
																				    [common_inputs['''attention_mask'''], torch.ones(lowerCAmelCase__					,     lowerCAmelCase__					,     dtype=lowerCAmelCase__					)]					,     dim=1					)
																				SCREAMING_SNAKE_CASE       :   int         =      [
																				    (torch.zeros(lowerCAmelCase__					), torch.zeros(lowerCAmelCase__					)) for _ in range(lowerCAmelCase__					)
																				]
													return common_inputs







						def        __lowercase     (	self :   Union[str, Any]					,     lowerCAmelCase__ :   PreTrainedTokenizer					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   bool = False					,     lowerCAmelCase__ :   Optional[TensorType] = None					,     ):

													"""simple docstring"""
													# Copied from OnnxConfig.generate_dummy_inputs
													# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													SCREAMING_SNAKE_CASE       :   Any         =      compute_effective_axis_dimension(
													    lowerCAmelCase__					,     fixed_dimension=OnnxConfig.default_fixed_batch					,     num_token_to_add=0					)

													# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
													SCREAMING_SNAKE_CASE       :   Tuple         =      tokenizer.num_special_tokens_to_add(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      compute_effective_axis_dimension(
													    lowerCAmelCase__					,     fixed_dimension=OnnxConfig.default_fixed_sequence					,     num_token_to_add=lowerCAmelCase__					)

													# Generate dummy inputs according to compute batch and sequence
													SCREAMING_SNAKE_CASE       :   str         =      [''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      dict(tokenizer(lowerCAmelCase__					,     return_tensors=lowerCAmelCase__					)					)
													return common_inputs







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   PreTrainedTokenizer					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   int = -1					,     lowerCAmelCase__ :   bool = False					,     lowerCAmelCase__ :   Optional[TensorType] = None					,     ):

													"""simple docstring"""
													if self.task in ["default", "seq2seq-lm"]:
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																				    lowerCAmelCase__					,     batch_size=lowerCAmelCase__					,     seq_length=lowerCAmelCase__					,     is_pair=lowerCAmelCase__					,     framework=lowerCAmelCase__					)

													else:
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      self._generate_dummy_inputs_for_causal_lm(
																				    lowerCAmelCase__					,     batch_size=lowerCAmelCase__					,     seq_length=lowerCAmelCase__					,     is_pair=lowerCAmelCase__					,     framework=lowerCAmelCase__					)

													return common_inputs







						def        __lowercase     (	self :   Optional[Any]					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Any					,     lowerCAmelCase__ :   Union[str, Any]					):

													"""simple docstring"""
													if self.task in ["default", "seq2seq-lm"]:
																				SCREAMING_SNAKE_CASE       :   Tuple         =      super()._flatten_past_key_values_(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   str         =      super(lowerCAmelCase__					,     self					)._flatten_past_key_values_(
																				    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)







						@property
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													return 1e-4









'''simple docstring'''



import argparse
from collections import defaultdict

import yaml


lowerCAmelCase_  :							Tuple             =    'docs/source/en/_toctree.yml'
def        UpperCAmelCase (    A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   Dict         =      defaultdict(A					)
							for doc in model_doc:
														counts[doc["local"]] += 1
							SCREAMING_SNAKE_CASE       :   Tuple         =      [key for key, value in counts.items() if value > 1]

							SCREAMING_SNAKE_CASE       :   Tuple         =      []
							for duplicate_key in duplicates:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}					)
														if len(A					) > 1:
																					raise ValueError(
																					    F"""{duplicate_key} is present several times in the documentation table of content at """
																					    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																					    '''others.'''					)
														# Only add this once
														new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]					)

							# Sort
							return sorted(A       ,		key=lambda A					: s["title"].lower()					)
def        UpperCAmelCase (    A	:					Any=False					):
							with open(A       ,		encoding='''utf-8'''					) as f:
														SCREAMING_SNAKE_CASE       :   str         =      yaml.safe_load(f.read()					)

							# Get to the API doc
							SCREAMING_SNAKE_CASE       :   Any         =      0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							SCREAMING_SNAKE_CASE       :   Dict         =      content[api_idx]['''sections''']

							# Then to the model doc
							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							SCREAMING_SNAKE_CASE       :   Dict         =      api_doc[model_idx]['''sections''']

							SCREAMING_SNAKE_CASE       :   List[Any]         =      [(idx, section) for idx, section in enumerate(A					) if '''sections''' in section]
							SCREAMING_SNAKE_CASE       :   int         =      False
							for idx, modality_doc in modalities_docs:
														SCREAMING_SNAKE_CASE       :   List[str]         =      modality_doc['''sections''']
														SCREAMING_SNAKE_CASE       :   List[str]         =      clean_model_doc_toc(A					)

														if old_modality_doc != new_modality_doc:
																					SCREAMING_SNAKE_CASE       :   Dict         =      True
																					if overwrite:
																												SCREAMING_SNAKE_CASE       :   int         =      new_modality_doc

							if diff:
														if overwrite:
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      model_doc
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      api_doc
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as f:
																												f.write(yaml.dump(A       ,		allow_unicode=A					)					)
														else:
																					raise ValueError(
																					    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Optional[int]             =    argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							lowerCAmelCase_  :							Any             =    parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




